In [1]:
from load_data import loading_and_pre_processing_pipeline
from dotenv import load_dotenv
import os

load_dotenv()

feature_data = (
    loading_and_pre_processing_pipeline()
    .sort_values("user_id")
    .head(n=int(os.environ["LIMIT"]))
)

print(
    f'{len(feature_data)} records from {len(set(feature_data["user_id"].values))} users present. '
)
print(f"{len(feature_data.dropna(axis=0))} of which are complete.")
feature_data.head()

________________________________________________________________________________
[Memory] Calling load_data.pull_from_postgres...
pull_from_postgres(Composed([SQL('\n        SELECT\n            signal_mean '), Identifier('rhr_signal_mean'), SQL(', \n            signal_min '), Identifier('rhr_signal_min'), SQL(', \n            signal_max '), Identifier('rhr_signal_max'), SQL(', \n            date test_week_start, user_id\n        FROM\n            datenspende_derivatives.vital_features\n        WHERE\n            type='), Literal(65), SQL(' AND\n            baseline_count>30 AND\n            signal_count > 3\n        ')]))

/home/kolb/.cache/pypoetry/virtualenvs/ml-nowcasting-ZsA4wecP-py3.8/lib/python3.8/site-packages/pandas/io/sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy
  warnings.warn(

_______________________________________________pull_from_postgres - 3.4s, 0.1min
________________________________________________________________________________
[Memory] Calling load_data.pull_from_postgres...
pull_from_postgres(Composed([SQL('\n        SELECT\n            signal_mean '), Identifier('steps_signal_mean'), SQL(', \n            signal_min '), Identifier('steps_signal_min'), SQL(', \n            signal_max '), Identifier('steps_signal_max'), SQL(', \n            date test_week_start, user_id\n        FROM\n            datenspende_derivatives.vital_features\n        WHERE\n            type='), Literal(9), SQL(' AND\n            baseline_count>30 AND\n            signal_count > 3\n        ')]))

/home/kolb/.cache/pypoetry/virtualenvs/ml-nowcasting-ZsA4wecP-py3.8/lib/python3.8/site-packages/pandas/io/sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy
  warnings.warn(

_______________________________________________pull_from_postgres - 4.5s, 0.1min
________________________________________________________________________________
[Memory] Calling load_data.pull_from_postgres...
pull_from_postgres(SQL("\n            SELECT\n                test_week_start,\n                user_id,\n                administered_vaccine_doses as vaccination_status,\n                days_since_last_dose,\n                f40 as chills,\n                f41 as body_pain,\n                f42 as loss_of_taste_and_smell,\n                f43 as fatigue,\n                f44 as cough,\n                f45 as cold,\n                f46 as diarrhea,\n                f47 as sore_throat,\n                f49 as asymptomatic,\n                f10 as test_result,\n                f76 as fittness,\n                f127 as sex,\n                f133 as age\n\n            FROM\n                datenspende_derivatives.homogenized_features\n            -- WHERE test_week_start > '2022-01-01'\n            "))

/home/kolb/.cache/pypoetry/virtualenvs/ml-nowcasting-ZsA4wecP-py3.8/lib/python3.8/site-packages/pandas/io/sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy
  warnings.warn(

_______________________________________________pull_from_postgres - 4.1s, 0.1min
275831 records from 22219 users present. 
0 of which are complete.
Out[1]:
test_week_start omicronba1_share omicronba2_share delta_share user_id rhr_signal_mean rhr_signal_min rhr_signal_max steps_signal_mean steps_signal_min ... cough cold diarrhea sore_throat asymptomatic test_result fittness sex age date
55175 2021-12-17 16.571429 NaN 82.9 250.0 0.560620 -0.564515 1.343627 -0.068585 -1.130044 ... False False False False False None NaN NaN NaN 2021-12-17
267803 2022-06-18 0.263158 NaN 0.0 250.0 0.651161 -0.733985 2.766732 -0.244201 -1.364146 ... False False False False True None NaN NaN NaN 2022-06-18
158988 2022-03-12 19.014286 NaN 0.0 250.0 -0.128217 -1.496442 1.032321 -0.152217 -0.953820 ... False False False False True None NaN NaN NaN 2022-03-12
215887 2022-04-23 1.242857 NaN 0.0 250.0 -0.881925 -2.220484 -0.238711 0.083447 -1.028943 ... False False False False True None NaN NaN NaN 2022-04-23
10070 2021-10-31 0.000000 NaN 100.0 250.0 0.022953 -1.542625 1.280464 0.074694 -1.149278 ... False False False False False None NaN NaN NaN 2021-10-31

5 rows × 27 columns

In [2]:
numeric = [
    "rhr_signal_mean",
    "rhr_signal_min",
    "rhr_signal_max",
    "steps_signal_mean",
    "steps_signal_min",
    "steps_signal_max",
    "days_since_last_dose",
    "vaccination_status",
    "fittness",
    "sex",
    "age",
    "omicronba1_share",
    "omicronba2_share",
    "delta_share",
]
categorical = [
    "chills",
    "body_pain",
    "loss_of_taste_and_smell",
    "fatigue",
    "cough",
    "cold",
    "diarrhea",
    "sore_throat",
]

feature_names = categorical + numeric
target = "test_result"
In [3]:
import pandas as pd
from sklearn.model_selection import GroupShuffleSplit

from sklearn.linear_model import LogisticRegression
from typing import List

from sklearn.compose import make_column_transformer
from sklearn.compose import make_column_selector as selector

from imblearn.pipeline import make_pipeline as make_pipeline_with_sampler

from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder, PolynomialFeatures
from sklearn.pipeline import make_pipeline


def train_model(features: List[str], target: str, data: pd.DataFrame, random_state=42):

    # use only records with labels (test_result) present
    working_data = data.dropna(subset=[target])

    # Do train test split along user ids meaning the data of one group of users is the test set and the
    # data of another group of users is the training set and data of one particular user can not be in both.
    gsp = GroupShuffleSplit(test_size=0.33, n_splits=1, random_state=random_state)
    split = gsp.split(working_data, groups=working_data["user_id"])
    train, test = next(split)

    training_data = working_data.iloc[train]
    testing_data = working_data.iloc[test]

    X = training_data[features]
    y = training_data[target].astype(int)

    # pipe = make_pipeline(StandardScaler(), LogisticRegression())
    # impute missing values: numeric values with their mean and boolean values (reported symptoms) with False
    num_pipe = make_pipeline(
        StandardScaler(), SimpleImputer(strategy="mean", add_indicator=True)
    )

    # TODO: Try out encoding missing symptoms as False (Dummy encoding with default to Zero)
    cat_pipe = make_pipeline(
        SimpleImputer(strategy="constant", fill_value=False),
        OneHotEncoder(),
    )
    preprocessor_linear = make_column_transformer(
        [num_pipe, selector(dtype_include="number")],
        [cat_pipe, selector(dtype_include="object")],
        n_jobs=2,
    )

    # use polynomial features to enable the model to fit different sets of parameters for different virus variants
    # Also use class_weight='balanced' in logistic regression to correct for imbalance between positive and negative test results.
    pipe = make_pipeline_with_sampler(
        preprocessor_linear,
        PolynomialFeatures(interaction_only=True),
        LogisticRegression(max_iter=1000, class_weight="balanced"),
    )

    pipe.fit(X, y)

    return (
        pipe,
        testing_data[features],
        testing_data[target].astype(int),
        test,
        list(set(list(training_data["user_id"].values))),
    )
In [4]:
from analysis_plots import plot_analysis

pipeline, X_test, y_test, _, __ = train_model(feature_names, target, feature_data)

fig = plot_analysis(pipeline, X_test, y_test, feature_names, 0.5)

fig.savefig("model_metrics.png", dpi=300)

resample
precission-recall
roc-auc
permutation-feature-importance

/home/kolb/.cache/pypoetry/virtualenvs/ml-nowcasting-ZsA4wecP-py3.8/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; Function :func:`plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: :meth:`sklearn.metric.RocCurveDisplay.from_predictions` or :meth:`sklearn.metric.RocCurveDisplay.from_estimator`.
  warnings.warn(msg, category=FutureWarning)

confusion-matrix
Text(0, 0, '2308')
In [5]:
import ramda as R
from datetime import timedelta

MEAN_DAYS = 7


def calculate_incidence(cases_column: str, cases_data: pd.DataFrame):

    res2 = cases_data.groupby("date").agg({cases_column: ["sum", "count"]})

    res2.columns = res2.columns.droplevel(0)

    res2["rate"] = res2["sum"].div(res2["count"])

    res2["incidence"] = res2.rolling("7D")["rate"].mean() * 100_000

    incidence = res2.reset_index().dropna(axis=0)[["incidence", "date"]]
    incidence["date"] = incidence["date"] + timedelta(days=7)
    return incidence


def calculate_questionnaire_incidence(
    feature_data: pd.DataFrame,
) -> pd.DataFrame:

    res1 = feature_data.replace(
        {True: 1, False: 0, None: 0, float("nan"): 0}
    ).reset_index()[["test_result", "date", "user_id"]]

    return calculate_incidence("test_result", res1)


@R.curry
def predicted_incidence(
    classifier,
    feature_data,
    feature_names,
    threshold,
    column_name="rolling mean detections",
):
    data = feature_data.set_index(["user_id", "date"])

    # predicted probability for a set of vital data and reported symptoms to represent an infection
    proba_infected = classifier.predict_proba(data[feature_names]).T[1].T

    p = pd.DataFrame(data=proba_infected, index=data.index, columns=["probability"])

    colname = f"{MEAN_DAYS}d {column_name}"

    # threshold predicted probabilities to classify data points in positive and negative predictions
    p[colname] = p["probability"] > threshold

    # if individuals are detected as infected in more than one week, take only the first detection
    only_first_positive_detections = (
        p[p[colname] == True]
        .reset_index()
        .sort_values(["user_id", "date"])
        .groupby(["user_id"])
        .agg({colname: "first", "date": lambda x: list(x)[0]})
        .reset_index()
    )
    all_negative_detections = p[p[colname] == False].reset_index()

    detected_infections = pd.concat(
        [only_first_positive_detections, all_negative_detections]
    )

    return calculate_incidence(colname, detected_infections)


def predict_incidence_runs(
    feature_data: pd.DataFrame, feature_names, threshold=0.2
) -> pd.DataFrame:
    runs = []
    for i in range(10):
        predictor, _, __, ___, training_user_ids = train_model(
            feature_names, target, feature_data, random_state=i
        )

        full_test_data = feature_data[~feature_data["user_id"].isin(training_user_ids)]
        test_data_with_tests = full_test_data[full_test_data["test_result"].notna()]
        test_data_without_tests = full_test_data[full_test_data["test_result"].isna()]

        print(full_test_data["test_result"])

        nowcasted_incidence_all_data = predicted_incidence(
            predictor, full_test_data, feature_names, threshold
        )

        nowcasted_incidence_no_test = predicted_incidence(
            predictor, test_data_without_tests, feature_names, threshold
        )

        nowcasted_incidence_with_test = predicted_incidence(
            predictor, test_data_with_tests, feature_names, threshold
        )

        nowcasted_incidence_all_data["run_id"] = i
        nowcasted_incidence_no_test["run_id"] = i
        nowcasted_incidence_with_test["run_id"] = i

        nowcasted_incidence_all_data["data"] = "all"
        nowcasted_incidence_no_test["data"] = "no_test"
        nowcasted_incidence_with_test["data"] = "with_test"

        runs.append(
            nowcasted_incidence_all_data[["incidence", "run_id", "date", "data"]]
        )
        runs.append(
            nowcasted_incidence_no_test[["incidence", "run_id", "date", "data"]]
        )
        runs.append(
            nowcasted_incidence_with_test[["incidence", "run_id", "date", "data"]]
        )

    predicted_incidence_runs = pd.concat(runs).reset_index().sort_values("date")

    predicted_incidence_plot_data = (
        predicted_incidence_runs.groupby(["date", "data"])
        .agg({"incidence": ["mean", "std"]})
        .reset_index()
    )

    predicted_incidence_plot_data.columns = ["date", "data", "mean", "std"]
    return predicted_incidence_plot_data
In [6]:
import plotly.graph_objects as go
from datetime import date, datetime

# All
threshold = 0.6
after = feature_data[["date"]] > pd.to_datetime(date(year=2021, month=11, day=1))
before = feature_data[["date"]] < pd.to_datetime(
    datetime.now().date() - timedelta(days=1)
)

working_data = feature_data[(before & after).values]

questionnaire_incidence = calculate_questionnaire_incidence(working_data)
predicted_incidence_plot_data = predict_incidence_runs(
    working_data, feature_names, threshold
)
predicted_incidence_plot_data.replace({float("nan"): 0})

55175      None
267803     None
158988     None
215887     None
245521     None
          ...  
269746     None
271401    False
272365    False
273252     None
273253     None
Name: test_result, Length: 100140, dtype: object
30622     None
267804    None
227575    None
194811    None
231670    None
          ... 
269746    None
268664    True
270573    None
273252    None
273253    None
Name: test_result, Length: 101214, dtype: object
55175     None
267803    None
158988    None
215887    None
245521    None
          ... 
269745    None
273251    None
269746    None
273252    None
273253    None
Name: test_result, Length: 99936, dtype: object
55175     None
267803    None
158988    None
215887    None
245521    None
          ... 
269746    None
268664    True
270573    None
273252    None
273253    None
Name: test_result, Length: 99938, dtype: object
55175      None
267803     None
158988     None
215887     None
245521     None
          ...  
270573     None
271401    False
272365    False
273252     None
273253     None
Name: test_result, Length: 99522, dtype: object
30622     None
267804    None
227575    None
194811    None
231670    None
          ... 
269745    None
273251    None
269746    None
273252    None
273253    None
Name: test_result, Length: 99611, dtype: object
30622      None
267804     None
227575     None
194811     None
231670     None
          ...  
268664     True
270573     None
271401    False
273252     None
273253     None
Name: test_result, Length: 100678, dtype: object
30622     None
267804    None
227575    None
194811    None
231670    None
          ... 
269745    None
273251    None
269746    None
273252    None
273253    None
Name: test_result, Length: 99080, dtype: object
55175      None
267803     None
158988     None
215887     None
245521     None
          ...  
270573     None
271401    False
272365    False
273252     None
273253     None
Name: test_result, Length: 99509, dtype: object
30622     None
267804    None
227575    None
194811    None
231670    None
          ... 
269746    None
268664    True
270573    None
273252    None
273253    None
Name: test_result, Length: 99961, dtype: object
Out[6]:
date data mean std
0 2021-11-09 all 606.597968 449.603057
1 2021-11-09 no_test 82.444147 173.991390
2 2021-11-09 with_test 2267.219182 1778.158130
3 2021-11-10 all 1011.526158 415.726550
4 2021-11-10 no_test 397.938583 258.242355
... ... ... ... ...
697 2022-06-29 no_test 2557.715461 263.527321
698 2022-06-29 with_test 8612.219523 753.504225
699 2022-06-30 all 4002.390985 155.271376
700 2022-06-30 no_test 2309.093896 299.155844
701 2022-06-30 with_test 8407.178220 636.331020

702 rows × 4 columns

In [7]:
predicted_incidence_plot_data.sort_values("date", inplace=True)

default_layout = {
    "title": {"y": 0.97, "x": 0.5, "xanchor": "center", "yanchor": "top"},
    "font_size": 12,
    "autosize": True,
    "height": 600,
    "margin": dict(l=60, r=30, b=75, t=80, pad=0),
    "template": "plotly_white",
    "hovermode": "x",
    "legend": {
        "font_size": 11,
        "x": 0,
        "y": 1,
        "itemclick": "toggleothers",
        "itemdoubleclick": "toggle",
        "tracegroupgap": 2,
    },
    "hoverlabel": {"font_color": "white", "namelength": -1},
    "xaxis": {
        "showline": True,
        "linewidth": 1.5,
        "linecolor": "#455266",
        "ticks": "outside",
        "tickwidth": 1.5,
        "rangemode": "nonnegative",
    },
    "yaxis": {
        "showline": True,
        "linewidth": 1.5,
        "linecolor": "#455266",
        "ticks": "outside",
        "tickwidth": 1.5,
        # "rangemode": "nonnegative",
        # "range": [0, 12_000],
    },
    "showlegend": True,
}

i18n = {
    "de": {
        "booster": "Mit Auffrischimpfung 18-59 Jahre",
        "base": "Grundimmunisierte  18-59 Jahre",
        "unvax": "Ungeimpfte 18-59 Jahre",
        "title": "Gemeldete Inzidenz vs. Machine Learning Nowcast",
        "y_axis_title": "Inzidenz",
        "all": "Nowcast",
        "no_test": "Without data with tests",
        "with_test": "Only data with tests",
        "insecurity": "Modell Unsicherheit",
    },
    "en": {
        "booster": "With booster 18-59 years",
        "base": "Vaccinated  18-59 years",
        "unvax": "Unvaccinated 18-59 years",
        "title": "Reported Incidence vs. Machine Learning Nowcast",
        "y_axis_title": "Incidence",
        "all": "Nowcast",
        "no_test": "Nur daten mit Tests",
        "with_test": "Nur daten ohne Tests",
        "insecurity": "Confidence interval",
    },
}


SKIP_DAYS = 5


@R.curry
def add_line(fig, data: pd.DataFrame, config: dict):
    key = config["key"]
    hue_color = config["hue_color"]
    line_color = config["line_color"]

    x = list(data[data["data"] == key]["date"].apply(pd.Timestamp))
    y = data[data["data"] == key]["mean"].values
    y_err = data[data["data"] == key]["std"].values
    y_upper = list(y + y_err)
    y_lower = list(y - y_err)

    print(x)

    fig.add_trace(
        go.Scatter(
            name=translation["insecurity"],
            x=x[::-1] + x,  # x, then x reversed
            y=y_lower[::-1] + y_upper,  # upper, then lower reversed
            showlegend=False,
            legendgroup=translation[key],
            marker=dict(color="#444"),
            line=dict(width=1, color=line_color),
            mode="lines",
            fillcolor=hue_color,  #
            fill="toself",
            hoverinfo="skip",
        )
    )

    fig.add_trace(
        go.Scatter(
            y=y,
            x=x,
            name=translation[key],
            legendgroup=translation[key],
            mode="lines",
            hovertemplate="%{y:.0f}",
            line=dict(width=2.5, color=line_color),  #
        )
    )


for language, translation in i18n.items():
    fig = go.Figure()

    R.map(
        add_line(fig, predicted_incidence_plot_data),
        [
            {
                "key": "all",
                "hue_color": "rgba(76, 89, 168, 0.3)",
                "line_color": "#4C59A8",
            },
            {
                "key": "no_test",
                "hue_color": "rgba(245, 240, 85, 0.3)",
                "line_color": "#F5F055",
            },
            {
                "key": "with_test",
                "hue_color": "rgba(236, 73, 245, 0.3)",
                "line_color": "#EC49F5",
            },
        ],
    )

    fig.add_trace(
        go.Scatter(
            y=questionnaire_incidence["incidence"].values[:-1],
            x=questionnaire_incidence["date"].values[:-1],
            name="questionnaire incidence",
            legendgroup="incidence",
            mode="lines",
            hovertemplate="%{y:.0f}",
            line=dict(width=2.5, color="red"),  # 455266
        )
    )
    layout = dict(
        title_text=translation["title"],
        yaxis_title=translation["y_axis_title"],
        hovermode="x unified",
        hoverlabel={
            "font": {
                "color": "black",
            }
        },
    )
    fig.update_layout(default_layout)
    fig.update_layout(layout)
    fig.write_html(
        f"incidence_nowcast_{language}.html",
        include_plotlyjs="cdn",
        full_html=False,
        config={"responsive": "true", "displayModeBar": False},
    )

fig.write_image(f"incidence_nowcast.png")
fig.show()

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In [ ]: